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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Federated Machine Learning-Based Anomaly Detection System for Synchrophasor Network Using Heterogeneous Data Sets: Preprint

Synchrophasor technology is widely deployed in the energy management system to monitor the grid health at micro level and perform necessary corrective actions in real time; however, integrated phasor devices and data aggregators are exposed to several cybersecurity threats. This paper proposes a federated ML(FML)-based ADS to detect several data integrity attacks in the synchrophasor network. The proposed approach integrates the horizontal FML technique and consists of substation-based local models and a control center-based global model. The proposed methodology includes training local models using heterogeneous data sets that include network and grid information and updating the global model through multiple iterations by sharing model gradients. Finally, the trained global model is applied to identify cyberattacks, normal operation, and physical events. To validate the proof of concept, we used synthetic data sets generated by Mississippi State University and Oak Ridge National Laboratory for training and testing the classification models using the National Renewable Energy Laboratory's high performance computing resources. Our experimental results, computed through several performance measures, reveal that the proposed approach shows consistent performance during the binary, three-class, and multiclass classifications while ensuring privacy of synchrophasor data.

anomaly detection system↗
NASA NTRS1993

A function approximation approach to anomaly detection in propulsion system test data

Ground test data from propulsion systems such as the Space Shuttle Main Engine (SSME) can be automatically screened for anomalies by a neural network. The neural network screens data after being trained with nominal data only. Given the values of 14 measurements reflecting external influences on the SSME at a given time, the neural network predicts the expected nominal value of a desired engine parameter at that time. We compared the ability of three different function-approximation techniques to perform this nominal value prediction: a novel neural network architecture based on Gaussian bar basis functions, a conventional back propagation neural network, and linear regression. These three techniques were tested with real data from six SSME ground tests containing two anomalies. The basis function network trained more rapidly than back propagation. It yielded nominal predictions with, a tight enough confidence interval to distinguish anomalous deviations from the nominal fluctuations in an engine parameter. Since the function-approximation approach requires nominal training data only, it is capable of detecting unknown classes of anomalies for which training data is not available.

Whitehead, Bruce A.↗
DOE OSTI2021

Smart Inverter Twin Model for Anomaly Detection

Smart inverters connected to a communication network are vulnerable to various anomalies in the form of cyberattacks. In this paper, a self-security approach is implemented using the digital twin concept for smart inverters. The digital twin is formed using the inverter’s dynamic model. Then, the incoming setpoints are autonomously examined using the digital twin, and only the safe setpoints are engaged to the inverter’s local controller. This paper demonstrates the details of the self-security algorithm and how the inverter’s digital twin is formed. In particular, the stable and unstable operation region is experimentally verified by changing the power setpoints engaged to the local controller, using a laboratory setup including a three-phase 1.5-kVA SiC-MOSFET inverter and a 12-kW NHR 9410 regenerative power grid emulator. The results demonstrate that the digital twin model can potentially protect inverters from abnormal operation by examining the incoming commands (new setpoints) using the inverter’s digital twin before engaging the setpoints to the local controller.

Hossen, Tareq↗
DOE OSTI2022

An Ensemble Approach to Computationally Efficient Radiological Anomaly Detection and Isotope Identification

Radiological source search is a challenging task involving detection and identification of weak sources in a constantly changing radiological background. As of now, many radiological source detection algorithms have been proposed; however, their computational complexity, and hence reliance on power intensive processing units inhibit low-power applications of radiological source search systems. In this work, we introduce the anomaly filter (AF) algorithm; a computationally light, yet effective time-series source detection algorithm based on exponential weighted moving average (EWMA) and Poisson deviance statistics. Then, we demonstrate that the proposed algorithm can be used in ensemble with other more computationally intensive source detection and identification algorithms to achieve both increased detection performance and reduced power consumption. The proposed AF algorithm and the ensemble algorithms were thoroughly benchmarked against several existing source detection and identification algorithms. The results show that the AF algorithm outperforms existing conventional source detection algorithms, and the ensemble approach improves the overall performance of existing source detection and isotope identification algorithms. Furthermore, the AF algorithm and the Non-negative Matrix Factorization approach based source identification (NMF-ID) algorithm were combined and implemented on a singleboard microcontroller and the power consumption was measured. This ensemble algorithm reduced the power consumption of the NMF-ID algorithm almost by a factor of 100, while improving the detection performance of the overall system.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Delegated Regressor, A Robust Approach for Automated Anomaly Detection in the Soil Radon Time Series Data

We propose a new method based on the idea of delegating regressors for predicting the soil radon gas concentration (SRGC) and anomalies in radon or any other time series data. The proposed method is compared to different traditional boosting e.g., Extreme Gradient Boosting (EGB) and simple regression methods e.g., support vector regressors with linear kernel and radial kernel in terms of accurate predictions. R language has been used for the statistical analysis of radon time series (RTS) data. The results obtained show that the proposed methodology predicts SRGC more accurately when compared to different traditional boosting and regression methods. The best correlation is found between the actual and predicted radon concentration for window size of 2 i.e., two days before and after the start of seismic activities. RTS data was collected from 05 February 2017 to 16 February 2018, including 7 seismic events recorded during the study period. Findings of study show that the proposed methodology predicts the SRGC with more precision, for all the window sizes, by overlapping predicted with the actual radon time series concentrations.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1979

Lunar magnetic anomalies detected by the Apollo subsatellite magnetometers

Properties of lunar crustal magnetization thus far deduced from Apollo subsatellite magnetometer data are reviewed using two of the most accurate available magnetic anomaly maps, one covering a portion of the lunar near side and the other a part of the far side. The largest single anomaly found within the region of coverage on the near-side map correlates exactly with a conspicuous light-colored marking in western Oceanus Procellarum called Reiner Gamma. This feature is interpreted as an unusual deposit of ejecta from secondary craters of the large nearby primary impact crater Cavalerius. The mean altitude of the far-side anomaly gap is much higher than that of the near side map and the surface geology is more complex; individual anomaly sources have therefore not yet been identified. The mechanism of magnetization and the origin of the magnetizing field remain unresolved, but the uniformity with which the Reiner Gamma deposit is apparently magnetized, and the north-south depletion of magnetization intensity across a substantial portion of the far side, seem to require the existence of an ambient field, perhaps of global or larger extent.

Hood, L. L.↗
NASA NTRS2012

Anomaly Detection Techniques with Real Test Data from a Spinning Turbine Engine-Like Rotor

Online detection techniques to monitor the health of rotating engine components are becoming increasingly attractive to aircraft engine manufacturers in order to increase safety of operation and lower maintenance costs. Health monitoring remains a challenge to easily implement, especially in the presence of scattered loading conditions, crack size, component geometry, and materials properties. The current trend, however, is to utilize noninvasive types of health monitoring or nondestructive techniques to detect hidden flaws and mini-cracks before any catastrophic event occurs. These techniques go further to evaluate material discontinuities and other anomalies that have grown to the level of critical defects that can lead to failure. Generally, health monitoring is highly dependent on sensor systems capable of performing in various engine environmental conditions and able to transmit a signal upon a predetermined crack length, while acting in a neutral form upon the overall performance of the engine system.

Abdul-Aziz, Ali↗
DOE OSTI2025

Anomaly Detection On Network Traffic Using A Sequence Model Approach

The code ingests Zeek logs derived from network packet captures, applies a DBSCAN model, Gower distance to build a feature set as input into a transformer model that will output anomaly scores and provides the option to set a threshold as to what’s considered an anomaly.

Quach, Anna [Idaho National Laboratory (INL), Idah↗
NASA NTRS1995

Investigation of the collision line broadening problem as applicable to the NASA Optical Plume Anomaly Detection (OPAD) system, phase 1

As a final report for phase 1 of the project, the researchers are submitting to the Tennessee Tech Office of Research the following two papers (reprinted in this report): 'Collision Line Broadening Effects on Spectrometric Data from the Optical Plume Anomaly System (OPAD),' presented at the 30th AIAA/ASME/SAE/ASEE Joint Propulsion Conference, 27-29 June 1994, and 'Calculation of Collision Cross Sections for Atomic Line Broadening in the Plume of the Space Shuttle Main Engine (SSME),' presented at the IEEE Southeastcon '95, 26-29 March 1995. These papers fully state the problem and the progress made up to the end of NASA Fiscal Year 1994. The NASA OPAD system was devised to predict concentrations of anomalous species in the plume of the Space Shuttle Main Engine (SSME) through analysis of spectrometric data. The self absorption of the radiation of these plume anomalies is highly dependent on the line shape of the atomic transition of interest. The Collision Line Broadening paper discusses the methods used to predict line shapes of atomic transitions in the environment of a rocket plume. The Voigt profile is used as the line shape factor since both Doppler and collisional line broadening are significant. Methods used to determine the collisional cross sections are discussed and the results are given and compared with experimental data. These collisional cross sections are then incorporated into the current self absorbing radiative model and the predicted spectrum is compared to actual spectral data collected from the Stennis Space Center Diagnostic Test Facility rocket engine. The second paper included in this report investigates an analytical method for determining the cross sections for collision line broadening by molecular perturbers, using effective central force interaction potentials. These cross sections are determined for several atomic species with H2, one of the principal constituents of the SSME plume environment, and compared with experimental data.

Dean, Timothy C.↗
NASA NTRS2004

Concept for Inclusion of Analytical and Computational Capability in Optical Plume Anomaly Detection (OPAD) for Measurement of Neutron Flux

Researchers are working on many fronts to make possible high-speed, automated classification and quantification of constituent materials in numerous environments. NASA's Marshall Space Flight Center has implemented a system for rocket engine flowfields/plumes. The Optical Plume Anomaly Detector (OPAD) system was designed to utilize emission and absorption spectroscopy for monitoring molecular and atomic particulates in gas plasma. An accompanying suite of tools and analytical package designed to utilize information collected by OPAD is known as the Engine Diagnostic Filtering System (EDiFiS). The current combination of these systems identifies atomic and molecular species and quantifies mass loss rates in H2/O2 rocket plumes. Capabilities for real-time processing are being advanced on several fronts, including an effort to hardware encode components of the EDiFiS for health monitoring and management. This paper addresses the OPAD with its tool suites, and discusses what is considered a natural progression: a concept for taking OPAD to the next logical level of high energy physics, incorporating fermion and boson particle analyses in measurement of neutron flux.

Patrick, Marshall Clint↗
DOE OSTI2020

An Analysis of the Statistics and Systematics of Limb Anomaly Detections in HST/STIS Transit Images of Europa

Several recent studies derived the existence of plumes on Jupiter’s moon Europa. The only technique that provided multiple detections is the far-ultraviolet imaging observations of Europa in transit of Jupiter taken by the Space Telescope Imaging Spectrograph (STIS) on the Hubble Space Telescope (HST). In this study, we reanalyze the three HST/STIS transit images in which Sparks et al. identified limb anomalies as evidence for Europa’s plume activity. After reproducing the results of Sparks et al., we find that positive outliers are similarly present in the images as the negative outliers that were attributed to plume absorption. A physical explanation for the positive outliers is missing. We then investigate the systematic uncertainties and statistics in the images and identify two factors that are crucial when searching for anomalies around the limb. One factor is the alignment between the actual and assumed locations of Europa on the detector. A misalignment introduces distorted statistics, most strongly affecting the limb above the darker trailing hemisphere where the plumes were detected. The second factor is a discrepancy between the observation and the model used for comparison, adding uncertainty in the statistics. When accounting for these two factors, the limb minima (and maxima) are consistent with random statistical occurrence in a sample size given by the number of pixels in the analyzed limb region. The plume candidate features in the three analyzed images can be explained by purely statistical fluctuations and do not provide evidence for absorption by plumes.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2012

Aircraft Anomaly Detection Using Performance Models Trained on Fleet Data

This paper describes an application of data mining technology called Distributed Fleet Monitoring (DFM) to Flight Operational Quality Assurance (FOQA) data collected from a fleet of commercial aircraft. DFM transforms the data into aircraft performance models, flight-to-flight trends, and individual flight anomalies by fitting a multi-level regression model to the data. The model represents aircraft flight performance and takes into account fixed effects: flight-to-flight and vehicle-to-vehicle variability. The regression parameters include aerodynamic coefficients and other aircraft performance parameters that are usually identified by aircraft manufacturers in flight tests. Using DFM, the multi-terabyte FOQA data set with half-million flights was processed in a few hours. The anomalies found include wrong values of competed variables, (e.g., aircraft weight), sensor failures and baises, failures, biases, and trends in flight actuators. These anomalies were missed by the existing airline monitoring of FOQA data exceedances.

Gorinevsky, Dimitry↗
DOE OSTI2026

Explainable machine learning for incipient anomaly detection in compact molten salt heat exchanger with overlapping feature distributions

High-temperature molten salt-cooled reactors (MSCRs) are a promising next-generation nuclear technology option, offering efficient power conversion and inherent safety features. However, the reliability of these systems depends on the robust operation of heat exchangers (HXs), which are susceptible to failure due to temperature gradients and channel plugging caused by fluid freezing. Conventional monitoring methods, relying on inlet and outlet measurements, lack the spatial resolution needed to detect early-stage faults. We propose a novel design of a compact salt-to-salt matrix-type HX design consisting of interleaved arrays of parallel tubes, with integrated synthetic fiber optic distributed temperature sensing (DTS) to enable localized detection of incipient faults. To evaluate performance of this design, we generate high-fidelity synthetic data using heat transfer computational modeling to simulate channel plugging, and introduce sensor noise for realistic modeling of measurements. The dataset comprises of 97% normal operation and 3% anomaly cases, with each anomaly class representing 1% of the data. These early anomalies result in overlapping temperature profiles between normal and faulty channels, producing a non-separable dataset that challenges traditional classification techniques. We benchmark eight supervised machine learning (ML) models and demonstrate that XGBoost achieves the highest performance. To improve transparency, we develop an explainability framework combining Shapley values and partially ordered sets (POSETs) to quantify and structurally analyze feature importance. This approach identifies both dominant predictors and ambiguous feature relationships, enhancing trust and interpretability. Our results highlight the potential of combining DTS and explainable ML with intelligent feature selection to improve predictive maintenance and ensure operational resilience in advanced nuclear systems.

Prantikos, Konstantinos [Argonne National Laborato↗
NASA NTRS2018

Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding

As spacecraft send back increasing amounts of telemetry data, improved anomaly detection systems are needed to lessen the monitoring burden placed on operations engineers and reduce operational risk. Current spacecraft monitoring systems only target a subset of anomaly types and often require costly expert knowledge to develop and maintain due to challenges involving scale and complexity. We demonstrate the effectiveness of Long Short-Term Memory (LSTMs) networks, a type of Recurrent Neural Network (RNN), in overcoming these issues using expert-labeled telemetry anomaly data from the Soil Moisture Active Passive (SMAP) satellite and the Mars Science Laboratory (MSL) rover, Curiosity. We also propose a complementary unsupervised and nonparametric anomaly thresholding approach developed during a pilot implementation of an anomaly detection system for SMAP, and offer false positive mitigation strategies along with other key improvements and lessons learned during development.

Soderstrom, Tom↗
DOE OSTI2026

NMF-Based Anomaly Detection in CMS 2D Tracking Occupancy Histograms

The CMS experiment relies on Data Quality Monitoring (DQM) to ensure that recorded collision data are suitable for physics analysis. During LHC Run 3, each run contains many lumisections and tracking monitoring elements, making offline inspection challenging, especially for localized detector effects that may appear only for short periods of time. This poster presents an unsupervised machine-learning approach to identify anomalous lumisections in CMS tracking occupancy histograms using Non-Negative Matrix Factorization (NMF). The workflow uses offline CMS DQMIO tracking histograms retrieved with the CMS DIALS API and organized as two-dimensional occupancy maps for each lumisection. After selecting stable lumisections, the occupancy maps are normalized and arranged into a non-negative data matrix. The NMF model learns a compact set of basis patterns describing normal tracking occupancy. Each lumisection is then reconstructed from these learned components, and the reconstruction error is used as an anomaly score. Large residuals indicate occupancy patterns that deviate from normal detector behavior and are flagged for further inspection. This NMF-based approach provides a fast and interpretable way to flag lumisections whose tracking occupancy patterns differ from normal detector behavior. Preliminary studies show sensitivity to known tracking anomalies, and ongoing work is focused on validating the method across additional Run 3 Pixel and Strip detector issues.

Rodríguez Ramos, Iliomar [Puerto Rico U., Mayaguez↗
DOE OSTI2023

Simulation-based anomaly detection for multileptons at the LHC

Decays of Higgs boson-like particles into multileptons is a well-motivated process for investigating physics beyond the Standard Model (SM). A unique feature of this final state is the precision with which the SM is known. As a result, simulations are used directly to estimate the background. Current searches consider specific models and typically focus on those with a single free parameter to simplify the analysis and interpretation. In this paper, we explore recent proposals for signal model agnostic searches using machine learning in the multilepton final state. These tools can be used to simultaneously search for many models, some of which have no dedicated search at the Large Hadron Collider. We find that the machine learning methods offer broad coverage across parameter space beyond where current searches are sensitive, with a necessary loss of performance compared to dedicated searches by only about one order of magnitude.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗